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Tier-1 brand, mid-level generalist ML title, metro location, and broad skill requirements increase applicant competition.
Core ML engineering skills transfer across industries, but ads/recommendation and GenAI production experience creates moderate domain bias.
Explicit degree-plus-years and extensive mandatory MLOps, big-data, LLM, and production engineering skills imply high filter strictness.
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Design, build, and operate scalable backend and ML systems for sponsored experiences, ranking, retrieval, and personalization.
Develop and own MLOps pipelines for continuous integration, delivery, training, validation, and monitoring of production ML models.
Engineer data pipelines and infrastructure to train and serve traditional ML and Generative AI models at scale with low latency.
MS in Computer Science or related field with 5+ years or BS/BA with 6+ years relevant experience in ML/AI/Data Engineering.
Expertise in object-oriented programming languages such as Scala, Java, or Python.
Experience with big data distributed processing frameworks (Apache Hadoop, Spark, Flink).
Proven ability to build production-grade ML systems including CI/CD pipelines, and operate live ML services reliably.
Experienced in translating research prototypes into scalable production ML services in a high-throughput environment.
Strong background in production machine learning engineering with cloud, containerization (Docker, Kubernetes), and ML serving technologies.
Comfortable working collaboratively with applied researchers, product managers, and engineering teams to deliver robust AI-powered ecommerce systems.